Many approaches have been developed to aid practitioners in distinguishing malicious files (i.e., malware) from benign ones. Early solutions centered on employing static or dynamic analyses of binaries to identify indicators of malicious behavior, e.g., stealing user credentials [1]}. However, each faces significant drawbacks: static analysis relies on manually-specified signatures that struggle to generalize to newer malware variants, while dynamic analyses bring high computational costs and virtual environments that are detectable by malicious programs (which can then fly under the radar) [2]}. More recently, a slew of data-driven strategies have been developed to sidestep the above issues by training ML models to distinguish between benign and malicious executables [3]}, [4]}, [2]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}.
